Recurrence Quantification Analysis for Investigating Atrial Fibrillation Dynamics in a Heterogeneous Simulation Setup^.

Area of Science:

• Computational cardiology
• Nonlinear dynamics analysis
• Electrophysiology modeling

Background:

• Persistent atrial fibrillation (AF) ablation outcomes are suboptimal due to complex AF dynamics.
• Investigating these dynamics is crucial for improving therapeutic strategies.

Purpose of the Study:

• To investigate atrial fibrillation dynamics using recurrence quantification analysis (RQA) in a heterogeneous simulation.
• To characterize dynamic patterns associated with different rotor activities in the left atrium.

Main Methods:

• Simulated AF in a left atrial model, extracting unipolar atrial electrograms (AEGs).
• Calculated phase, identified points of singularity, and defined regions based on rotor persistence (none, transient, long-standing).
• Computed RQA attributes (determinism, recurrence rate, laminarity), Sample Entropy, and Dominant Frequency from filtered bipolar AEGs.

Main Results:

• Regions with long-standing rotors exhibited significantly lower RQA attributes and Sample Entropy, indicating higher irregularity (P≤0.01).
• Both normal and fractionated AEGs were present in all regions, with significant differences between regions with no rotors and long-standing rotors.
• RQA identified distinct dynamic patterns related to regularity and nonstationary behavior, irrespective of AEG classification.

Conclusions:

• Long-standing rotors in AF simulations are associated with complex, irregular dynamics.
• The co-existence of normal and fractionated AEGs within regions of long-standing rotors was observed.
• RQA is a valuable tool for uncovering underlying dynamic patterns in AF, aiding in understanding and potentially improving ablation strategies.